1. G. Thirel, L. Coron, V. Andréassian, C. Perrin
Application of several hydrological models (and objective functions) to the complete dataset of the workshop
G. Thirel, L. Coron, V. Andréassian, C. Perrin
22 July 2013

2. Introduction What is the main issue when we fail on non-stationarity?
Models?
Objective functions?
Something else?
Application of 3 models
Application of 6 objective functions
IAHS Hw15
22 July 2013

3. Outline of this presentation
Impact of using different models
The models
The results
Impact of using different objective functions
The objective functions
IAHS Hw15
22 July 2013

4. Outline of this presentation
Impact of using different models
The models
The results
Impact of using different objective functions
The objective functions
IAHS Hw15
22 July 2013

5. GR4J and GR5J Lumped conceptual models, resp. 4 and 5 parameters GR4J
IAHS Hw15
22 July 2013

6. MORDOR6
Lumped conceptual model with 6 parameters (simplification of the MORDOR model).
IAHS Hw15
22 July 2013

7. The snow module
No snow module: Axe Creek, Gilbert, Flinders, Wimmera and Bani Rivers.
CemaNeige: all the other basins.
CemaNeige = degree-day model, 2 free parameters.
IAHS Hw15
22 July 2013

8. The objective function
The Nash on root square of discharge is used in this part.
IAHS Hw15
22 July 2013

9. Outline of this presentation
Impact of using different models
The models
The results
Impact of using different objective functions
The objective functions
IAHS Hw15
22 July 2013

10. Rivers with T increase High flows
IAHS Hw15
GR4J and GR5J are the best for the Kamp, except during P2
No big difference for the Garonne
22 July 2013

11. Rivers with T increase MORDOR6 misses the 2002 Kamp flood
Observed peak value
MORDOR6 peak values
IAHS Hw15
22 July 2013

12. Rivers with T increase Low flows GR5J the best for the Kamp
IAHS Hw15
GR5J the best for the Kamp
MORDOR6 and GR5J the best for the Garonne
22 July 2013

13. Rivers with T increase
Variability due to model and calibration choices
GR4J
GR5J
Kamp
MORDOR6
IAHS Hw15
22 July 2013
The model choice and calibration induce the same order of variability

14. Rivers with discharge change or high variability
Best performance for GR5J
Wimmera
High flows
IAHS Hw15
22 July 2013

15. Rivers with discharge change or high variability
No model has the « solution » for handling the Millenium Drought
Wimmera
IAHS Hw15
22 July 2013

16. Rivers with T increase
Variability due to model and calibration choices
GR4J
GR5J
Wimmera
MORDOR6
IAHS Hw15
22 July 2013
The model choice and calibration induce the same order of variability

17. Rivers with discharge change or high variability
Severe crash from GR4J due to high reactivity
Attempts to increase the reaction time or to better initialize the parameters all failed
The structure of GR4J (&GR5J) is to revise for such a basin
Bani
GR4J
IAHS Hw15
22 July 2013
MORDOR6

18. Outline of this presentation
Impact of using different models
The models
The results
Impact of using different objective functions
The objective functions
IAHS Hw15
22 July 2013

19. The objective functions
For this part only the gr4j model is used
Inverse of discharge
Square root of discharge
Discharge
Nash
NaIQ
NaRQ
NaQ
KGE
KGEIQ
KGERQ
KGEQ
IAHS Hw15
22 July 2013

20. Outline of this presentation
Impact of using different models
The models
The results
Impact of using different objective functions
The objective functions
IAHS Hw15
22 July 2013

21. Rivers with T increase
Calibrating on IQ gives the lowest Nash(Q) -> of course!
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22 July 2013

22. Rivers with T increase
Calibrating on Q gives the lowest Nash(IQ) -> of course!
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22 July 2013

23. Rivers with P decrease Kamp
Variability due to calibration and Objective function choices
NaQ
NaRQ
NaIQ
KGEQ
KGERQ
KGEIQ
IAHS Hw15
The objective functions impact the model bias more than the model choice
22 July 2013

24. Rivers with discharge change or high variability
Only NaRQ does not show disastrous results on P5 when calibrated on wet period.
KGERQ performs the best on wet periods when calibrated on P5.
Wimmera
IAHS Hw15
22 July 2013
Prod. Store: NaRQ > KGERQ
Loss for P5: KGERQ > NaRQ

25. Rivers with P decrease Wimmera
Variability due to calibration and Objective function choices
NaQ
NaRQ
NaIQ
KGEQ
KGERQ
KGEIQ
IAHS Hw15
The objective functions strongly impact the model bias
22 July 2013

26. Conclusions Attempts to quantify the (un-)stability induced by :
The model choice –> low impact
The calibration period -> low impact on variability, high impact on bias
The objective function -> huge impact
IAHS Hw15
22 July 2013

27. Thank you!